glatos_detections: Construct, check, and validate a glatos_detections object

In ocean-tracking-network/glatos: A package for the Great Lakes Acoustic Telemetry Observation System

Construct, check, and validate a glatos_detections object

Description

Creates, checks, or validates a glatos_detections object.

Usage

glatos_detections(..., validate = TRUE)

as_glatos_detections(x, validate = TRUE)

is_glatos_detections(x)

validate_glatos_detections(x)

Arguments

...	Named vectors, minimally one for each required column of the specified class: animal_id must be character, indifies unique individual animal. detection_timestamp_utc must be POSIXct, timestamps(in UTC) of detection. deploy_lat must be numeric, latitude, in decimal degrees, WGS84, southern hemisphere is negative. deploy_long must be numeric, longitude, in decimal degrees, WGS84, western hemisphere is negative.
validate	logical, indicates if column names and classes should be checked against requirements.
x	A data.frame or object that inherits from data.frame (e.g., data.table, tibble) and contains all required columns (see ...).

Construction

glatos_detections() creates a glatos_detections object from individual vectors (one for each column) and optionally checks for required column names and classes using validate_glatos_detections().

Coercion

as_glatos_detections() coerces a data.frame, or object that inherits from data.frame, to glatos_detections and optionally checks for required column names and classes using validate_glatos_detections().

Validation

is_glatos_detections() checks class attribute for "glatos_detections"

validate_glatos_detections() checks for required column names and classes

Examples

#  glatos_detections
x <- data.frame(
  animal_id = c("153", "153", "153", "153"),
  detection_timestamp_utc = as.POSIXct(
    c(
      "2012-04-29 01:48:37",
      "2012-04-29 01:52:55",
      "2012-04-29 01:55:12",
      "2012-04-29 01:56:42"
    ),
    tz = "UTC"
  ),
  deploy_lat = c(43.39165, 43.39165, 43.39165, 43.39165),
  deploy_long = c(-83.99264, -83.99264, -83.99264, -83.99264)
)

gd_df1 <- glatos_detections(
  animal_id = x$animal_id,
  detection_timestamp_utc =
    x$detection_timestamp_utc,
  deploy_lat = x$deploy_lat,
  deploy_long = x$deploy_long
)


# as_glatos_detections
gd_df2 <- as_glatos_detections(x)


# sf input

library(sf)

# use remove = FALSE to keep required columns
x_sf <- sf::st_as_sf(x,
  coords = c("deploy_long", "deploy_lat"),
  remove = FALSE
)

gd_sf <- as_glatos_detections(x_sf)


# tibble input
library(dplyr)

x_tbl <- dplyr::as_tibble(x)

gd_tbl <- as_glatos_detections(x_tbl)


# All below will error as invalid

# data.frame input; missing column name
library(dplyr) # for rename
x2 <- rename(x,
  fish_id = animal_id,
  det_date_time = detection_timestamp_utc
)

try(
  gd2 <- as_glatos_detections(x2)
)

# data.frame input; wrong column class
x3 <- mutate(x,
  animal_id = as.integer(animal_id),
  detection_timestamp_utc = as.character(detection_timestamp_utc)
)

try(
  gr3 <- as_glatos_detections(x3)
)

# Validation and checking

validate_glatos_detections(x)

is_glatos_detections(x) # FALSE

is_glatos_detections(gd_df1) # TRUE

ocean-tracking-network/glatos documentation built on April 17, 2025, 10:38 p.m.